Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	132,069
2	South Dakota	128,820
3	Rhode Island	122,333
4	Utah	117,383
5	Arizona	113,842
6	Tennessee	113,137
7	Oklahoma	108,732
8	Iowa	108,199
9	Arkansas	107,926
10	Wisconsin	107,025
11	Nebraska	105,533
12	Kansas	103,065
13	South Carolina	102,641
14	Alabama	102,259
15	Mississippi	100,278
16	Indiana	99,883
17	Idaho	97,579
18	Nevada	96,493
19	Illinois	95,200
20	Wyoming	95,055
21	Montana	94,850
22	Georgia	94,484
23	Louisiana	93,683
24	Texas	93,483
25	Missouri	93,222
26	Kentucky	93,203
27	New Jersey	92,636
28	California	91,389
29	Delaware	91,285
30	Florida	91,144
31	New Mexico	89,414
32	New York	88,070
33	Minnesota	87,431
34	Massachusetts	86,363
35	North Carolina	84,142
36	Ohio	84,137
37	Alaska	81,267
38	Connecticut	80,963
39	Colorado	76,685
40	Pennsylvania	75,056
41	West Virginia	74,858
42	Virginia	69,049
43	Michigan	66,342
44	Maryland	64,467
45	District of Columbia	59,519
46	New Hampshire	56,970
47	Washington	45,946
48	Puerto Rico	42,479
49	Oregon	37,546
50	Maine	34,409
51	Vermont	26,236
52	Hawaii	19,736

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Missouri	2,810
2	Rhode Island	463
3	New Jersey	383
4	New York	314
5	Connecticut	311
6	District of Columbia	277
7	Alaska	264
8	Idaho	255
9	Colorado	224
10	Michigan	219
11	Florida	195
12	Pennsylvania	194
13	North Carolina	189
14	Massachusetts	188
15	Delaware	183
16	Iowa	180
17	Minnesota	177
18	Tennessee	169
19	Texas	169
20	South Carolina	168
21	Kentucky	164
22	Kansas	155
23	Georgia	154
24	Utah	153
25	Vermont	153
26	New Hampshire	147
27	Nebraska	144
28	Ohio	143
29	Virginia	143
30	Wyoming	143
31	South Dakota	140
32	West Virginia	132
33	Montana	126
34	Louisiana	123
35	Maryland	123
36	Illinois	121
37	Maine	114
38	Washington	108
39	Alabama	107
40	Oklahoma	105
41	Nevada	101
42	Arizona	99
43	California	99
44	Arkansas	97
45	North Dakota	97
46	Indiana	96
47	Mississippi	96
48	New Mexico	89
49	Wisconsin	85
50	Oregon	79
51	Hawaii	28
52	Puerto Rico	24

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,667
2	New York	2,472
3	Rhode Island	2,415
4	Massachusetts	2,395
5	Mississippi	2,299
6	Arizona	2,253
7	Connecticut	2,174
8	South Dakota	2,152
9	Louisiana	2,110
10	Alabama	2,084
11	North Dakota	1,943
12	Pennsylvania	1,912
13	Indiana	1,897
14	New Mexico	1,831
15	Illinois	1,820
16	Arkansas	1,783
17	Iowa	1,775
18	South Carolina	1,705
19	Tennessee	1,684
20	Michigan	1,671
21	Kansas	1,665
22	Georgia	1,647
23	Nevada	1,645
24	Texas	1,584
25	Delaware	1,532
26	Ohio	1,510
27	Florida	1,487
28	District of Columbia	1,469
29	Missouri	1,422
30	California	1,388
31	Maryland	1,323
32	West Virginia	1,300
33	Montana	1,298
34	Wisconsin	1,228
35	Minnesota	1,200
36	Wyoming	1,193
37	Oklahoma	1,188
38	Nebraska	1,153
39	Virginia	1,153
40	Kentucky	1,124
41	North Carolina	1,108
42	Idaho	1,064
43	Colorado	1,057
44	New Hampshire	872
45	Washington	677
46	Puerto Rico	648
47	Utah	621
48	Oregon	548
49	Maine	537
50	Alaska	397
51	Vermont	338
52	Hawaii	314

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Oklahoma	14
2	Minnesota	9
3	Virginia	9
4	Arkansas	6
5	Delaware	6
6	Missouri	6
7	Alabama	5
8	California	5
9	New Mexico	5
10	Rhode Island	5
11	Texas	5
12	Wyoming	5
13	Connecticut	4
14	Florida	4
15	Georgia	4
16	Iowa	4
17	Kansas	4
18	Kentucky	4
19	Louisiana	4
20	Maine	4
21	Massachusetts	4
22	Mississippi	4
23	New Jersey	4
24	New York	4
25	Ohio	4
26	Arizona	3
27	District of Columbia	3
28	Idaho	3
29	Nevada	3
30	North Carolina	3
31	Maryland	2
32	Pennsylvania	2
33	Tennessee	2
34	Washington	2
35	Wisconsin	2
36	Illinois	1
37	Indiana	1
38	Michigan	1
39	Montana	1
40	Nebraska	1
41	South Carolina	1
42	South Dakota	1
43	Utah	1
44	Vermont	1
45	Alaska	0
46	Colorado	0
47	Hawaii	0
48	New Hampshire	0
49	North Dakota	0
50	Oregon	0
51	Puerto Rico	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	342,023	1	99
Chattahoochee	Georgia	292,014	2	99
Bent	Colorado	265,734	3	99
Lincoln	Arkansas	242,629	4	99
Dewey	South Dakota	241,514	5	99
Davidson	Tennessee	131,346	227	92
Richland	South Carolina	101,347	1088	65
York	South Carolina	96,940	1279	59
Orange	California	82,852	1952	37
Pierce	Washington	43,637	2901	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Galax city	Virginia	7,878	3	99
Foard	Texas	7,792	4	99
Emporia city	Virginia	7,669	5	99
Orange	California	1,369	1836	41
Davidson	Tennessee	1,269	1965	37
Richland	South Carolina	1,224	2028	35
York	South Carolina	1,214	2047	34
Pierce	Washington	638	2707	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.